split community matrix into two dataframes- one for grouping variables and one for species biomass
#set up final grouping data into dataframe
ME_group_data<-trawl_data_arrange[, c(1,2,3,55,56,57,58)]
ME_NMDS_data<-as.matrix(trawl_data_arrange[,4:53])
ME_NMDS=metaMDS(ME_NMDS_data, # Our community-by-species matrix
k=2, # The number of reduced dimensions
trymax=200) #increase iterations
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.2370547
## Run 1 stress 0.237059
## ... Procrustes: rmse 0.0007026836 max resid 0.006237308
## ... Similar to previous best
## Run 2 stress 0.2374854
## ... Procrustes: rmse 0.01294161 max resid 0.176785
## Run 3 stress 0.2378877
## Run 4 stress 0.2379746
## Run 5 stress 0.2370549
## ... Procrustes: rmse 0.0004272761 max resid 0.004227707
## ... Similar to previous best
## Run 6 stress 0.2370988
## ... Procrustes: rmse 0.01530707 max resid 0.1770138
## Run 7 stress 0.2378253
## Run 8 stress 0.4164693
## Run 9 stress 0.237887
## Run 10 stress 0.237097
## ... Procrustes: rmse 0.01542374 max resid 0.177196
## Run 11 stress 0.2378878
## Run 12 stress 0.2371161
## ... Procrustes: rmse 0.006633357 max resid 0.08890372
## Run 13 stress 0.2371108
## ... Procrustes: rmse 0.006674069 max resid 0.08894954
## Run 14 stress 0.2377685
## Run 15 stress 0.2370549
## ... Procrustes: rmse 8.98838e-05 max resid 0.0008687876
## ... Similar to previous best
## Run 16 stress 0.2374778
## ... Procrustes: rmse 0.01322304 max resid 0.1769079
## Run 17 stress 0.2374838
## ... Procrustes: rmse 0.012942 max resid 0.1767773
## Run 18 stress 0.2443271
## Run 19 stress 0.2371161
## ... Procrustes: rmse 0.006631912 max resid 0.08890015
## Run 20 stress 0.2370549
## ... Procrustes: rmse 0.0004285777 max resid 0.004242401
## ... Similar to previous best
## *** Solution reached
#extract NMDS scores for ggplot
data.scores = as.data.frame(scores(ME_NMDS))
#add columns to data frame
data.scores$Stratum = trawl_data_arrange$Stratum
data.scores$Region = trawl_data_arrange$Region
data.scores$Year = trawl_data_arrange$Year
data.scores$Season= trawl_data_arrange$Season
data.scores$Year_groups= trawl_data_arrange$YEAR_GROUPS
data.scores$Year_decades= trawl_data_arrange$YEAR_DECADES
data.scores$Region_new=trawl_data_arrange$REGION_NEW
data.scores$Region_year=trawl_data_arrange$REGION_YEAR
data.scores$Season_year=trawl_data_arrange$SEASON_YEAR